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Economic model predictive control of stochastic nonlinear systems
Author(s) -
Wu Zhe,
Zhang Junfeng,
Zhang Zhihao,
Albalawi Fahad,
Durand Helen,
Mahmood Maaz,
Mhaskar Prashant,
Christofides Panagiotis D.
Publication year - 2018
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16167
Subject(s) - lyapunov function , control theory (sociology) , nonlinear system , model predictive control , controller (irrigation) , stability (learning theory) , stochastic control , stochastic process , state space , bounded function , mathematical optimization , stochastic modelling , computer science , mathematics , control (management) , optimal control , artificial intelligence , machine learning , mathematical analysis , statistics , physics , quantum mechanics , agronomy , biology
This work focuses on the design of stochastic Lyapunov‐based economic model predictive control (SLEMPC) systems for a broad class of stochastic nonlinear systems with input constraints. Under the assumption of stabilizability of the origin of the stochastic nonlinear system via a stochastic Lyapunov‐based control law, an economic model predictive controller is proposed that utilizes suitable constraints based on the stochastic Lyapunov‐based controller to ensure economic optimality, feasibility and stability in probability in a well‐characterized region of the state‐space surrounding the origin. A chemical process example is used to illustrate the application of the approach and demonstrate its economic benefits with respect to an EMPC scheme that treats the disturbances in a deterministic, bounded manner. © 2018 American Institute of Chemical Engineers AIChE J , 64: 3312–3322, 2018